AB1569 THE USE OF MACHINE LEARNING IN DIAGNOSING AND DETECTING DAMAGE IN THE HANDS AND FEET OF PATIENTS WITH RHEUMATOID ARTHRITIS AND PSORIATIC ARTHRITIS: A SCOPING REVIEW
Authors: A. Antony, A. Biju, A. Rambojun, W. Tillett
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Machine learning methods have the potential to optimise the diagnosis of inflammatory arthropathies and the quantification of damage over time. <h3>Objectives</h3> To summarise the literature investigating the use of machine learning methods to diagnose and detect damage in the hands and feet of patients with Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PsA). <h3>Methods</h3> A literature search was undertaken in PubMed, EMBASE, Web of Science and the Cochrane Central Register for Controlled Trials using a pre-specified search strategy. Full-text articles and conference abstracts published in English between the 1<sup>st</sup> of January 1995 and the 22<sup>nd</sup> of June 2022 that utilised machine learning (ML) in an imaging modality (X-Ray [XR], Computed Tomography [CT], Ultrasound [USS] or Magnetic Resonance Imaging [MRI]) to diagnose or assess structural damage in the hands and feet of patients with RA or PsA were included. All abstracts were reviewed by two authors (AA and AB) and all eligible full-text articles were assessed by the same authors, who subsequently independently extracted data according to the review protocol. Reference management and data extraction were conducted using Covidence. Data extracted included patient population and numbers, diagnoses, ML models and validation/testing methods, and algorithm performance. <h3>Results</h3> The literature review identified 851 unique references, of which 123 were eligible for full-text review; 26 references met the inclusion criteria for the review (Figure 1: PRISMA Diagram). There were 21 full-text articles and 5 conference abstracts published between 2000-2022, all of which assessed joints in the hands and/or wrists. Most studies utilised XR (n=23) rather than USS (n=1), MRI (n=1) and CT [1]. The number of patients/radiographs included used ranged from 15-5191 and most studies assessed RA only (n=23), with 2 studies assessing RA and PsA and 1 assessing PsA only. 24 studies were cross-sectional while 2 were longitudinal. ML methods were used to diagnose RA (n=9), diagnose PsA (n=2), estimate joint space score (n=5) or measure joint space width (n=3) or estimate change in joint space width (n=1), detect erosions (n=3) or estimate erosion score (n=7), estimate overall damage score (1), detect subluxation/ankylosis (n=1) and detect periarticular textural change (n=1). Additional tasks performed by ML algorithms were cropping of radiographs, localising joints and segmenting bones. Most studies utilised transfer learning on pre-trained neural networks: VGG15, VGG16, DenseNet, AlexNet, Single Shot Multibox Detector, YOLOv3, YOLOv4, LENet, Network in Network, SqueezeNet, ResNet, ResNet50, DeepTEN, U-net and EfficientNet. Some studies incorporated shape models (e.g. active shape, gaussian and local linear mapping models), multiscale gradient vector flow snakes, Graph Convolutional Networks, support vector machines, support vector regression and ridge regression. Complete model assessment with training, validation and testing cohorts were reported in 12 studies (46.2%), and of these 4 utilised n-fold cross-validation rather than hold-out validation. There was significant heterogeneity in the reporting and the performance of the models used. <h3>Conclusion</h3> The use of machine learning in diagnosing and detecting damage has mostly focused on plain radiography in patients with RA; there are limited data in PsA. Heterogeneity in the reporting of results limits succinct comparison of performance between ML methods. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Anna Antony Speakers bureau: Lilly, AbbVie, Ann Biju: None declared, Adwaye Rambojun: None declared, William Tillett Speakers bureau: Abbvie, Amgen, Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Consultant of: Abbvie, Amgen, Eli Lilly, GSK, Janssen, Novartis, Ono Pharma, Pfizer, UCB, Grant/research support from: Janssen, UCB, Pfizer, Eli-Lilly.